TY - JOUR
T1 - Clinical biomarkers differentiate myelitis from vascular and other causes of myelopathy
AU - Barreras, Paula
AU - Fitzgerald, Kathryn C.
AU - Mealy, Maureen A.
AU - Jimenez, Jorge A.
AU - Becker, Daniel
AU - Newsome, Scott D.
AU - Levy, Michael
AU - Gailloud, Philippe
AU - Pardo, Carlos A.
N1 - Funding Information:
This work was supported by The Bart McLean Fund for Neuroimmunology Research, Johns Hopkins Project Restore, and the Transverse Myelitis Association.
Funding Information:
The study was funded by the Bart McLean Fund for Neuro-immunology Research, Johns Hopkins Project Restore, and the Transverse Myelitis Association. Several authors report receiving research funding, personal compensation, and/or advisory committee appointments from various pharmaceutical companies, medical device manufacturers, and scholarly associations. Go to Neurology.org/N for full disclosures.
Publisher Copyright:
Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
PY - 2018/1
Y1 - 2018/1
N2 - Objective To assess the predictive value of the initial clinical and paraclinical features in the differentiation of inflammatory myelopathies from other causes of myelopathy in patients with initial diagnosis of transverse myelitis (TM). Methods We analyzed the clinical presentation, spinal cord MRI, and CSF features in a cohort of 457 patients referred to a specialized myelopathy center with the presumptive diagnosis of TM. After evaluation, the myelopathies were classified as inflammatory, ischemic/stroke, arteriovenous malformations/fistulas, spondylotic, or other. A multivariable logistic regression model was used to determine characteristics associated with the final diagnosis and predictors that would improve classification accuracy. Results Out of 457 patients referred as TM, only 247 (54%) were confirmed as inflammatory; the remaining 46% were diagnosed as vascular (20%), spondylotic (8%), or other myelopathy (18%). Our predictive model identified the temporal profile of symptom presentation (hyperacute <6 hours, acute 6-48 hours, subacute 48 hours-21 days, chronic >21 days), initial motor examination, and MRI lesion distribution as characteristics that improve the correct classification rate of myelopathies from 67% to 87% (multinomial area under the curve increased from 0.32 to 0.67), compared to only considering CSF pleocytosis and MRI gadolinium enhancement. Of all predictors, the temporal profile of symptoms contributed the most to the increased discriminatory power. Conclusions The temporal profile of symptoms serves as a clinical biomarker in the differential diagnosis of TM. The establishment of a definite diagnosis in TM requires a critical analysis of the MRI and CSF characteristics to rule out non-inflammatory causes of myelopathy.
AB - Objective To assess the predictive value of the initial clinical and paraclinical features in the differentiation of inflammatory myelopathies from other causes of myelopathy in patients with initial diagnosis of transverse myelitis (TM). Methods We analyzed the clinical presentation, spinal cord MRI, and CSF features in a cohort of 457 patients referred to a specialized myelopathy center with the presumptive diagnosis of TM. After evaluation, the myelopathies were classified as inflammatory, ischemic/stroke, arteriovenous malformations/fistulas, spondylotic, or other. A multivariable logistic regression model was used to determine characteristics associated with the final diagnosis and predictors that would improve classification accuracy. Results Out of 457 patients referred as TM, only 247 (54%) were confirmed as inflammatory; the remaining 46% were diagnosed as vascular (20%), spondylotic (8%), or other myelopathy (18%). Our predictive model identified the temporal profile of symptom presentation (hyperacute <6 hours, acute 6-48 hours, subacute 48 hours-21 days, chronic >21 days), initial motor examination, and MRI lesion distribution as characteristics that improve the correct classification rate of myelopathies from 67% to 87% (multinomial area under the curve increased from 0.32 to 0.67), compared to only considering CSF pleocytosis and MRI gadolinium enhancement. Of all predictors, the temporal profile of symptoms contributed the most to the increased discriminatory power. Conclusions The temporal profile of symptoms serves as a clinical biomarker in the differential diagnosis of TM. The establishment of a definite diagnosis in TM requires a critical analysis of the MRI and CSF characteristics to rule out non-inflammatory causes of myelopathy.
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U2 - 10.1212/WNL.0000000000004765
DO - 10.1212/WNL.0000000000004765
M3 - Article
C2 - 29196574
AN - SCOPUS:85050210006
SN - 0028-3878
VL - 90
SP - E12-E21
JO - Neurology
JF - Neurology
IS - 1
ER -